data windowing in orda technical briefing sebastián torres 1,2, chris curtis 1,2, rodger brown 2,...
TRANSCRIPT
Data Windowing in ORDA
Technical Briefing
Sebastián Torres1,2, Chris Curtis1,2,Rodger Brown2, and Michael Jain2
1Cooperative Institute for Mesoscale Meteorological Studies 2National Severe Storms Laboratory
NEXRAD Technical Advisory Committee MeetingMarch 27, 2007
What is Data Windowing? Unlike legacy RDA, ORDA uses data windows
Time-series samples used to compute base data moments are weighted using a “window”
Data windows available in ORDA are: rectangular (no window)
Equivalent to legacy RDA Hamming von Hann BlackmanM
ore
aggr
essi
ve
Rectangular Hammingvon Hann Blackman
Why Data Windowing? Tapered data windows are usually applied before
processing in the frequency domain (spectral processing) Aggressive windows have lower frequency sidelobes that
help reduce the frequency “leakage” effect In ORDA: GMAP, SZ-2
Tapered data windows reduce the effective antenna beamwidth due to scanning A narrower effective beamwidth results in smaller resolution
volumes In ORDA: Super Resolution
The Effects of Data Windowing
Tapered windows reduce the equivalent number of independent samples available to estimate spectral moments End samples “contribute less” to the estimation process
The more aggressive the data window, the larger the errors of estimates for all base data moments
RectangularWindow
HammingWindow
Standard Error of Moment Estimates for Different Windows vs. Spectrum Width (VCP 11)
Parameters correspond to 1st split cut of VCP 11
M = 17, Ts = 3.1 ms, SNR = 10 dB M = 52, Ts = 1 ms, SNR = 10 dB
Standard Error of Moment Estimates for Different Windows vs. SNR (VCP 11)
Parameters correspond to 1st split cut of VCP 11
M = 17, Ts = 3.1 ms, v = 4 m/s M = 52, Ts = 1 ms, v = 4 m/s
Standard Error of Moment Estimates for Different Windows vs. SNR (VCP 12)
Parameters correspond to 1st split cut of VCP 12
M = 15, Ts = 3.1 ms, v = 4 m/s M = 40, Ts = 1 ms, v = 4 m/s
Relative Error Increase for Different Data Windows
Compared to using a rectangular data window (no window), use of tapered data windows results in higher standard deviation of all base data moments For v = 4 m/s and high SNR
The Hamming window increases the errors by about 30% The von Hann window increases the errors by about 35% The Blackman window increases the errors by about 50%
A Timeline of Data Windowing in the RDA Past (before ORDA)
Rectangular: all legacy RDA algorithms Present (ORDA Builds 8 and 9)
Hamming: default window Blackman: GMAP ground clutter filter von Hann: SZ-2 with overlaid echoes
Near Future (ORDA Builds 10 through 12) Rectangular: proposed default window Blackman: GMAP ground clutter filter von Hann: SZ-2 with overlaid echoes and Super-Resolution
Future (ORDA Build 13 and beyond) Same as Build 12 plus… von Hann: spectral processing?
Windowing would not be necessary in case of unimodal spectra
How does the present system deviates from the past? Compare performance of
Rectangular vs. Hamming
VCP 11 Comparison
Surveillance Doppler Rectangular window Hamming window
Elv (deg) WF PRI M PRI M SD(Z) (dB)* SD(v) (m/s) SD(w) (m/s) SD(Z) (dB)* SD(v) (m/s) SD(w) (m/s)
0.5 CS 1 17 0.59 - - 0.80 - -0.5 CD 5 52 - 0.89 0.73 - 1.20 0.891.45 CS 1 16 0.61 - - 0.83 - -1.45 CD 5 52 - 0.89 0.73 - 1.20 0.892.4 B 1 6 5 41 0.94 0.63 1.01 0.86 1.31 0.84 1.34 1.043.35 B 2 6 5 41 1.04 0.63 1.01 0.86 1.42 0.84 1.34 1.044.3 B 2 6 5 41 1.04 0.63 1.01 0.86 1.42 0.84 1.34 1.045.25 B 3 10 5 41 0.97 0.63 1.01 0.86 1.29 0.84 1.34 1.046.2 B 3 10 5 41 0.97 0.63 1.01 0.86 1.29 0.84 1.34 1.047.5 CD 6 43 0.63 1.01 0.92 0.84 1.34 1.118.7 CD 7 46 0.63 1.01 0.98 0.85 1.34 1.1810 CD 7 46 0.63 1.01 0.98 0.85 1.34 1.1812 CD 7 46 0.63 1.01 0.98 0.85 1.34 1.1814 CD 7 46 0.63 1.01 0.98 0.85 1.34 1.18
16.7 CD 7 46 0.63 1.01 0.98 0.85 1.34 1.1819.5 CD 7 46 0.63 1.01 0.98 0.85 1.34 1.18
f = 2800 MHz, PRI Delta C * Reflectivity errors in the Batch mode are indicated for overlaid / non-overlaid situations.
Surveillance Doppler Rectangular window
Elv (deg) WF PRI M PRI M SD(Z) (dB)* SD(v) (m/s) SD(w) (m/s)
0.5 CS 1 17 0.59 - -0.5 CD 5 52 - 0.89 0.731.45 CS 1 16 0.61 - -1.45 CD 5 52 - 0.89 0.732.4 B 1 6 5 41 0.94 0.63 1.01 0.863.35 B 2 6 5 41 1.04 0.63 1.01 0.864.3 B 2 6 5 41 1.04 0.63 1.01 0.865.25 B 3 10 5 41 0.97 0.63 1.01 0.866.2 B 3 10 5 41 0.97 0.63 1.01 0.867.5 CD 6 43 0.63 1.01 0.928.7 CD 7 46 0.63 1.01 0.9810 CD 7 46 0.63 1.01 0.9812 CD 7 46 0.63 1.01 0.9814 CD 7 46 0.63 1.01 0.98
16.7 CD 7 46 0.63 1.01 0.9819.5 CD 7 46 0.63 1.01 0.98
Surveillance Doppler Rectangular window
Elv (deg) WF PRI M PRI M SD(Z) (dB)* SD(v) (m/s) SD(w) (m/s)
0.5 CS 1 17 0.59 - -0.5 CD 5 52 - 0.89 0.731.45 CS 1 16 0.61 - -1.45 CD 5 52 - 0.89 0.732.4 B 1 6 5 41 0.94 0.63 1.01 0.863.35 B 2 6 5 41 1.04 0.63 1.01 0.864.3 B 2 6 5 41 1.04 0.63 1.01 0.865.25 B 3 10 5 41 0.97 0.63 1.01 0.866.2 B 3 10 5 41 0.97 0.63 1.01 0.867.5 CD 6 43 0.63 1.01 0.928.7 CD 7 46 0.63 1.01 0.9810 CD 7 46 0.63 1.01 0.9812 CD 7 46 0.63 1.01 0.9814 CD 7 46 0.63 1.01 0.98
16.7 CD 7 46 0.63 1.01 0.9819.5 CD 7 46 0.63 1.01 0.98
NEXRAD Requirements and Historical Data Quality Meeting NEXRAD data quality requirements
Requirements formulated to ensure DQ is maintained within the system Committee established to assess NTR related to base data quality
Matching legacy RDA performance Algorithms and users were accustomed to data with a certain quality
Frequently, legacy RDA exceeded requirements Data quality of ORDA compared to legacy RDA determines the “DQ
Delta” that users and algorithms are experiencing Currently evaluating the operational impact of using a tapered data window
all the time (“apples-to-apples” comparison) Impact may be worse for
FAA’s fully-automated algorithms Data assimilation into numerical forecast models Polarimetric variables
Rectangularwindow
Algorithm Performance ComparisonOne-hour Precipitation Accumulation
Courtesy Bob Lee (ROC)
Hammingwindow
Algorithm Performance ComparisonOne-hour Precipitation Accumulation
Courtesy Bob Lee (ROC)
Algorithm Performance ComparisonMesocyclone Detection Algorithm
Rectangular windowHamming window
time
Acc
umul
ated
num
ber
of d
etec
tions
20%20% 20%20%20%20%
Range Gates with …
Clutter Overlaid echoesClean echoes
Split Cuts
Surveillancedata
Baseline processing
Super Resolution
Doppler data
Baseline processing
Super Resolution
SZ-2
Batch Cuts
Baseline processing for Long-PRT data
Baseline processing for Short-PRT data
Doppler Cuts Baseline processing
Range Gates with …
Clutter Overlaid echoesClean echoes
Split Cuts
Surveillancedata
Baseline processing Blackman - Default
Super Resolution Blackman - Von Hann
Doppler data
Baseline processing Blackman Default Default
Super Resolution Blackman Von Hann Von Hann
SZ-2 Blackman Von Hann Default
Batch Cuts
Baseline processing for Long-PRT data Rectangular - Rectangular
Baseline processing for Short-PRT data Blackman Default Default
Doppler Cuts Baseline processing Blackman - Default
Range Gates with …
Clutter Overlaid echoesClean echoes
Split Cuts
Surveillancedata
Baseline processing Blackman - Hamming
Super Resolution Blackman - Von Hann
Doppler data
Baseline processing Blackman Hamming Hamming
Super Resolution Blackman Von Hann Von Hann
SZ-2 Blackman Von Hann Hamming
Batch Cuts
Baseline processing for Long-PRT data Rectangular - Rectangular
Baseline processing for Short-PRT data Blackman Hamming Hamming
Doppler Cuts Baseline processing Blackman - Hamming
Range Gates with …
Clutter Overlaid echoesClean echoes
Split Cuts
Surveillancedata
Baseline processing Blackman - Rectangular
Super Resolution Blackman - Von Hann
Dopplerdata
Baseline processing Blackman Rectangular Rectangular
Super Resolution Blackman Von Hann Von Hann
SZ-2 Blackman Von Hann Rectangular
Batch Cuts
Baseline processing for Long-PRT data Rectangular - Rectangular
Baseline processing for Short-PRT data Blackman Rectangular Rectangular
Doppler Cuts Baseline processing Blackman - Rectangular
Near-Future Impact of Changing the Default Data Window in ORDA
Conclusions Hamming window increases errors of all base data moments
Compared to legacy RDA current data has ~30% larger errors VCPs that met NEXRAD DQ technical requirements with legacy RDA no
longer meet them with ORDA Is this lower data quality operationally acceptable?
Working on “apples-to-apples” quantitative analyses Preliminary results indicate there can be performance differences in certain
products Users always demand better data quality and faster updates
Back in 2003, one of the TAC’s recommendations was to… “Produce the best quality data possible from the WSR-88D throughout the remainder of its service life.” e.g., recommendation for super resolution followed this criterion (recombination)
Recommendations ORDA should operate with a rectangular window
whenever possible Making this change is straightforward, low-cost, and would not change the
computational complexity of ORDA algorithms Historically, requirements have been relaxed only if significant operational
benefits were realized Clutter filtering → Uncontaminated data VCP 12 → Faster updates SZ-2 → Recovery of overlaid echoes (reduced obscuration) Super Resolution → Improved detection of weather features
Operating with the Hamming window does not bring any significant operational benefits However, before using the rectangular window as the default window,
we recommend the ORDA spectrum width estimator be fixedStay tuned!
Relative Standard Error of Moment Estimates for Different Windows vs. Spectrum Width
Parameters correspond to 1st split cut of VCP 11
M = 17, Ts = 3.1 ms, SNR = 10 dB M = 52, Ts = 1 ms, SNR = 10 dB
Relative Standard Error of Moment Estimates for Different Windows vs. SNR
Parameters correspond to 1st split cut of VCP 11
M = 17, Ts = 3.1 ms, v = 4 m/s M = 52, Ts = 1 ms, v = 4 m/s
Errors with Different Data Windows for VCPs 11, 12, and 21
VCP 11SD(Z) (dB)
SD(v) (m/s)
SD(v) (m/s)
Rectangular 0.58 0.89 0.73
Hamming 0.80 1.19 0.90
Von Hann 0.78 1.21 0.91
Blackman 0.89 1.35 1.05
VCP 12SD(Z) (dB)
SD(v) (m/s)
SD(v) (m/s)
Rectangular 0.62 1.02 0.88
Hamming 0.84 1.36 1.06
Von Hann 0.82 1.37 1.08
Blackman 0.94 1.54 1.25
VCP 21SD(Z) (dB)
SD(v) (m/s)
SD(v) (m/s)
Rectangular 0.46 0.68 0.52
Hamming 0.62 0.91 0.66
Von Hann 0.62 0.93 0.68
Blackman 0.70 1.03 0.76
Errors correspond to the 1st elevation angleFor Z and v: v = 4 m/s and SNR = 10 dBFor v: v = 4 m/s and SNR = 8 dB
Relative Errors with Different Data Windows for VCPs 11, 12, and 21
VCP 11SD(Z) SD(Zrect)
SD(Zrect)
SD(v) SD(vrect)
SD(vrect)
SD(v) SD(v rect)
SD(v rect)
Hamming 38% 34% 23%
Von Hann 34% 36% 25%
Blackman 53% 52% 44%
Errors correspond to the 1st elevation angleFor Z and v: v = 4 m/s and SNR = 10 dBFor v: v = 4 m/s and SNR = 8 dB
VCP 12SD(Z) SD(Zrect)
SD(Zrect)
SD(v) SD(vrect)
SD(vrect)
SD(v) SD(v rect)
SD(v rect)
Hamming 35% 33% 20%
Von Hann 32% 34% 23%
Blackman 52% 51% 42%
VCP 21SD(Z) SD(Zrect)
SD(Zrect)
SD(v) SD(vrect)
SD(vrect)
SD(v) SD(v rect)
SD(v rect)
Hamming 35% 34% 27%
Von Hann 35% 37% 31%
Blackman 52% 51% 46%